OpenRaiser/Intern-Atlas huggingface dataset:
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers
Venue: arXiv cs.PL 2017
Year: 2017

Modern software systems in many application areas offer to the user a multitude of parameters, switches and other customisation hooks. Humans tend to have difficulties determining the best configurations for particular applications. Modern optimising compilers are an example of such software systems; their many parameters need to be tuned for optimal performance, but are often left at the default values for convenience. In this work, we automatically determine compiler parameter settings that result in optimised performance for particular applications. Specifically, we apply a state-of-the-art automated parameter configuration procedure based on cutting-edge machine learning and optimisation techniques to two prominent JavaScript compilers and demonstrate that significant performance improvements, more than 35% in some cases, can be achieved over the default parameter settings on a diverse set of benchmarks.
Approximation Schemes for Clustering with Outliers
Venue: arXiv cs.DS 2017
Year: 2017

Clustering problems are well-studied in a variety of fields such as data science, operations research, and computer science. Such problems include variants of centre location problems, $k$-median, and $k$-means to name a few. In some cases, not all data points need to be clustered; some may be discarded for various reasons. We study clustering problems with outliers. More specifically, we look at Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with outliers, we have to open some centres, discard up to $z$ points of $\cal X$ and assign every other point to the nearest open centre, minimizing the total assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have to open up to $k$ centres but there are no opening costs. In $k$-Means, the cost of assigning $j$ to $i$ is $δ^2(j,i)$. We present several results. Our main focus is on cases where $δ$ is a doubling metric or is the shortest path metrics of graphs from a minor-closed family of graphs. For uniform-cost UFL with outliers on such metrics we show that a multiswap simple local search heuristic yields a PTAS. With a bit more work, we extend this to bicriteria approximations for the $k$-Median and $k$-Means problems in the same metrics where, for any constant $ε> 0$, we can find a solution using $(1+ε)k$ centres whose cost is at most a $(1+ε)$-factor of the optimum and uses at most $z$ outliers. We also show that natural local search heuristics that do not violate the number of clusters and outliers for $k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics. Furthermore, we show how our analysis can be extended to general metrics for $k$-Means with outliers to obtain a $(25+ε,1+ε)$ bicriteria.
Strategic Coalitions with Perfect Recall
Venue: arXiv cs.AI 2017
Year: 2017

The paper proposes a bimodal logic that describes an interplay between distributed knowledge modality and coalition know-how modality. Unlike other similar systems, the one proposed here assumes perfect recall by all agents. Perfect recall is captured in the system by a single axiom. The main technical results are the soundness and the completeness theorems for the proposed logical system.
Model compression as constrained optimization, with application to neural nets. Part II: quantization
Venue: arXiv cs.LG 2017
Year: 2017

We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal. The codebook can be optimally learned jointly with the net, or fixed, as for binarization or ternarization approaches. Previous work has quantized the weights of the reference net, or incorporated rounding operations in the backpropagation algorithm, but this has no guarantee of converging to a loss-optimal, quantized net. We describe a new approach based on the recently proposed framework of model compression as constrained optimization \citep{Carreir17a}. This results in a simple iterative "learning-compression" algorithm, which alternates a step that learns a net of continuous weights with a step that quantizes (or binarizes/ternarizes) the weights, and is guaranteed to converge to local optimum of the loss for quantized nets. We develop algorithms for an adaptive codebook or a (partially) fixed codebook. The latter includes binarization, ternarization, powers-of-two and other important particular cases. We show experimentally that we can achieve much higher compression rates than previous quantization work (even using just 1 bit per weight) with negligible loss degradation.
Advances in Artificial Intelligence Require Progress Across all of Computer Science
Venue: arXiv cs.AI 2017
Year: 2017

Advances in Artificial Intelligence require progress across all of computer science.
Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification
Venue: arXiv cs.SD 2017
Year: 2017

Text-dependent speaker verification is becoming popular in the speaker recognition society. However, the conventional i-vector framework which has been successful for speaker identification and other similar tasks works relatively poorly in this task. Researchers have proposed several new methods to improve performance, but it is still unclear that which model is the best choice, especially when the pass-phrases are prompted during enrollment and test. In this paper, we introduce four modeling methods and compare their performance on the newly published RedDots dataset. To further explore the influence of different frame alignments, Viterbi and forward-backward algorithms are both used in the HMM-based models. Several bottleneck features are also investigated. Our experiments show that, by explicitly modeling the lexical content, the HMM-based modeling achieves good results in the fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and i-vector/HMM are not as successful. In both conditions, the forward-backward algorithm brings more benefits to the i-vector/HMM system. Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.
Symbolic Stochastic Chase Decoding of Reed-Solomon and BCH Codes
Venue: arXiv cs.IT 2017
Year: 2017

This paper proposes the Symbolic-Stochastic Chase Decoding Algorithm (S-SCA) for the Reed-Solomon (RS) and BCH codes. By efficient usage of void space between constellation points for $q$-ary modulations and using soft information at the input of the decoder, the S-SCA is capable of outperforming conventional Symbolic-Chase algorithm (S-CA) with less computational cost. Since the S-SCA starts with the randomized generation of likely test-vectors, it reduces the complexity to polynomial order and also it does not need to find the least reliable symbols to generate test-vectors. Our simulation results show that by increasing the number of test-vectors, the performance of the algorithm can approach the ML bound. The S-SCA($1K$) provides near $2$ dB gain in comparison with S-CA($1K$) for $(31, 25)$ RS code using $32$-QAM. Furthermore, the algorithm provides near $3$ dB further gain with $1K$ iteration compared with S-CA($65K$) when $(255, 239)$ RS code is used in an AWGN channel. For the Rayleigh fading channel and the same code, the algorithm provides more that $5$ dB gain. Also for $(63, 57)$ BCH codes and $8$-PSK modulation the proposed algorithm provides $3$dB gain with less complexity. This decoder is Soft-Input Soft-Output (SISO) decoder and is highly attractive in low power applications. Finally, the Symbolic-Search Bitwise-Transmission Stochastic Chase Algorithm (SSBT-SCA) was introduced for RS codes over BPSK transmission that is capable of generating symbolic test-vectors that reduce complexity and mitigate burst errors.
Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle
Venue: arXiv cs.CV 2017
Year: 2017

We tackle the problem of localizing an autonomous sea-surface vehicle in river estuarine areas using monocular camera and angular velocity input from an inertial sensor. Our method is challenged by two prominent drawbacks associated with the environment, which are typically not present in standard visual simultaneous localization and mapping (SLAM) applications on land (or air): a) Scene depth varies significantly (from a few meters to several kilometers) and, b) In conjunction to the latter, there exists no ground plane to provide features with enough disparity based on which to reliably detect motion. To that end, we use the IMU orientation feedback in order to re-cast the problem of visual localization without the mapping component, although the map can be implicitly obtained from the camera pose estimates. We find that our method produces reliable odometry estimates for trajectories several hundred meters long in the water. To compare the visual odometry estimates with GPS based ground truth, we interpolate the trajectory with splines on a common parameter and obtain position error in meters recovering an optimal affine transformation between the two splines.
Localization of Sound Sources in a Room with One Microphone
Venue: arXiv cs.SD 2017
Year: 2017

Estimation of the location of sound sources is usually done using microphone arrays. Such settings provide an environment where we know the difference between the received signals among different microphones in the terms of phase or attenuation, which enables localization of the sound sources. In our solution we exploit the properties of the room transfer function in order to localize a sound source inside a room with only one microphone. The shape of the room and the position of the microphone are assumed to be known. The design guidelines and limitations of the sensing matrix are given. Implementation is based on the sparsity in the terms of voxels in a room that are occupied by a source. What is especially interesting about our solution is that we provide localization of the sound sources not only in the horizontal plane, but in the terms of the 3D coordinates inside the room.
Game Theory for Secure Critical Interdependent Gas-Power-Water Infrastructure
Venue: arXiv cs.SY 2017
Year: 2017

A city's critical infrastructure such as gas, water, and power systems, are largely interdependent since they share energy, computing, and communication resources. This, in turn, makes it challenging to endow them with fool-proof security solutions. In this paper, a unified model for interdependent gas-power-water infrastructure is presented and the security of this model is studied using a novel game-theoretic framework. In particular, a zero-sum noncooperative game is formulated between a malicious attacker who seeks to simultaneously alter the states of the gas-power-water critical infrastructure to increase the power generation cost and a defender who allocates communication resources over its attack detection filters in local areas to monitor the infrastructure. At the mixed strategy Nash equilibrium of this game, numerical results show that the expected power generation cost deviation is 35\% lower than the one resulting from an equal allocation of resources over the local filters. The results also show that, at equilibrium, the interdependence of the power system on the natural gas and water systems can motivate the attacker to target the states of the water and natural gas systems to change the operational states of the power grid. Conversely, the defender allocates a portion of its resources to the water and natural gas states of the interdependent system to protect the grid from state deviations.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Venue: arXiv cs.CL 2017
Year: 2017

Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple $k$-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.
Outfix-guided insertion
Venue: arXiv cs.FL 2017
Year: 2017

Motivated by work on bio-operations on DNA strings, we consider an outfix-guided insertion operation that can be viewed as a generalization of the overlap assembly operation on strings studied previously. As the main result we construct a finite language $L$ such that the outfix-guided insertion closure of $L$ is non-regular. We consider also the closure properties of regular and (deterministic) context-free languages under the outfix-guided insertion operation and decision problems related to outfix-guided insertion. Deciding whether a language recognized by a deterministic finite automaton is closed under outfix-guided insertion can be done in polynomial time. The complexity of the corresponding question for nondeterministic finite automata remains open.
Annotated Defeasible Logic
Venue: arXiv cs.LO 2017
Year: 2017

Defeasible logics provide several linguistic features to support the expression of defeasible knowledge. There is also a wide variety of such logics, expressing different intuitions about defeasible reasoning. However, the logics can only combine in trivial ways. This limits their usefulness in contexts where different intuitions are at play in different aspects of a problem. In particular, in some legal settings, different actors have different burdens of proof, which might be expressed as reasoning in different defeasible logics. In this paper, we introduce annotated defeasible logic as a flexible formalism permitting multiple forms of defeasibility, and establish some properties of the formalism. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
Metis CCNx 1.0 Forwarder
Venue: arXiv cs.NI 2017
Year: 2017

Metis is the CCNx 1.0 forwarder that implements the CCNx 1.0 Semantics and Messages draft standards. This document describes how to use Metis and the internal software architecture.
Constructions of Optimal and Near-Optimal Quasi-Complementary Sequence Sets from an Almost Difference Set
Venue: arXiv cs.IT 2017
Year: 2017

Compared with the perfect complementary sequence sets, quasi-complementary sequence sets (QCSSs) can support more users to work in multicarrier CDMA communications. A near-optimal periodic QCSS is constructed in this paper by using an optimal quaternary sequence set and an almost difference set. With the change of the values of parameters in the almost difference set, the near-optimal QCSS can become asymptotically optimal and the number of users supported by the subcarrier channels in CDMA system has an exponential growth.
Automated Detection of Non-Relevant Posts on the Russian Imageboard "2ch": Importance of the Choice of Word Representations
Venue: arXiv cs.CL 2017
Year: 2017

This study considers the problem of automated detection of non-relevant posts on Web forums and discusses the approach of resolving this problem by approximation it with the task of detection of semantic relatedness between the given post and the opening post of the forum discussion thread. The approximated task could be resolved through learning the supervised classifier with a composed word embeddings of two posts. Considering that the success in this task could be quite sensitive to the choice of word representations, we propose a comparison of the performance of different word embedding models. We train 7 models (Word2Vec, Glove, Word2Vec-f, Wang2Vec, AdaGram, FastText, Swivel), evaluate embeddings produced by them on dataset of human judgements and compare their performance on the task of non-relevant posts detection. To make the comparison, we propose a dataset of semantic relatedness with posts from one of the most popular Russian Web forums, imageboard "2ch", which has challenging lexical and grammatical features.
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Venue: arXiv cs.CV 2017
Year: 2017

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.
Generative Adversarial Network based on Resnet for Conditional Image Restoration
Venue: arXiv cs.CV 2017
Year: 2017

The GANs promote an adversarive game to approximate complex and jointed example probability. The networks driven by noise generate fake examples to approximate realistic data distributions. Later the conditional GAN merges prior-conditions as input in order to transfer attribute vectors to the corresponding data. However, the CGAN is not designed to deal with the high dimension conditions since indirect guide of the learning is inefficiency. In this paper, we proposed a network ResGAN to generate fine images in terms of extremely degenerated images. The coarse images aligned to attributes are embedded as the generator inputs and classifier labels. In generative network, a straight path similar to the Resnet is cohered to directly transfer the coarse images to the higher layers. And adversarial training is circularly implemented to prevent degeneration of the generated images. Experimental results of applying the ResGAN to datasets MNIST, CIFAR10/100 and CELEBA show its higher accuracy to the state-of-art GANs.
Towards Affordable On-track Testing for Autonomous Vehicle - A Kriging-based Statistical Approach
Venue: arXiv cs.SY 2017
Year: 2017

This paper discusses the use of Kriging model in Automated Vehicle evaluation. We explore how a Kriging model can help reduce the number of experiments or simulations in the Accelerated Evaluation procedure. We also propose an adaptive sampling scheme for selecting samples to construct the Kriging model. Application examples in the lane change scenario are presented to illustrate the proposed methods.
Automatized Generation of Alphabets of Symbols
Venue: arXiv cs.HC 2017
Year: 2017

In this paper, we discuss the generation of symbols (and alphabets) based on specific user requirements (medium, priorities, type of information that needs to be conveyed). A framework for the generation of alphabets is proposed, and its use for the generation of a shorthand writing system is explored. We discuss the possible use of machine learning and genetic algorithms to gather inputs for generation of such alphabets and for optimization of already generated ones. The alphabets generated using such methods may be used in very different fields, from the creation of synthetic languages and constructed scripts to the creation of sensible commands for multimodal interaction through Human-Computer Interfaces, such as mouse gestures, touchpads, body gestures, eye-tracking cameras, and brain-computing Interfaces, especially in applications for elderly care and people with disabilities.
Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
Venue: arXiv cs.LG 2017
Year: 2017

Deep learning (deep structured learning, hierarchi- cal learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high- level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations. In this paper, we present the results of testing neural networks architectures on H2O platform for various activation functions, stopping metrics, and other parameters of machine learning algorithm. It was demonstrated for the use case of MNIST database of handwritten digits in single-threaded mode that blind selection of these parameters can hugely increase (by 2-3 orders) the runtime without the significant increase of precision. This result can have crucial influence for opitmization of available and new machine learning methods, especially for image recognition problems.
Multiplex Media Attention and Disregard Network among 129 Countries
Venue: arXiv cs.SI 2017
Year: 2017

We built a multiplex media attention and disregard network (MADN) among 129 countries over 212 days. By characterizing the MADN from multiple levels, we found that it is formed primarily by skewed, hierarchical, and asymmetric relationships. Also, we found strong evidence that our news world is becoming a "global village." However, at the same time, unique attention blocks of the Middle East and North Africa (MENA) region, as well as Russia and its neighbors, still exist.
Deep Learning to Attend to Risk in ICU
Venue: arXiv cs.LG 2017
Year: 2017

Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the clinician. Here we propose a deep learning architecture that can effectively handle these challenges for predicting ICU mortality outcomes. The model is based on Long Short-Term Memory, and has layered attention mechanisms. At the sensing layer, the model decides whether to observe and incorporate parts of the current measurements. At the reasoning layer, evidences across time steps are weighted and combined. The model is evaluated on the PhysioNet 2012 dataset showing competitive and interpretable results.
Characterizing User-to-User Connectivity with RIPE Atlas
Venue: arXiv cs.NI 2017
Year: 2017

Characterizing the interconnectivity of networks at a country level is an interesting but non-trivial task. The IXP Country Jedi is an existing prototype that uses RIPE Atlas probes in order to explore interconnectivity at a country level, taking into account all Autonomous Systems (AS) where RIPE Atlas probes are deployed. In this work, we build upon this basis and specifically focus on "eyeball" networks, i.e. the user-facing networks with the largest user populations in any given country, and explore to what extent we can provide insights on their interconnectivity. In particular, with a focused user-to-user (and/or user-to-content) version of the IXP Country Jedi we work towards meaningful statistics and comparisons between countries/economies. This is something that a general-purpose probe-to-probe version is not able to capture. We present our preliminary work on the estimation of RIPE Atlas coverage in eyeball networks, as well as an approach to measure and visualize user interconnectivity with our Eyeball Jedi tool.